# pylint: disable=E1101,E1103
# pylint: disable=W0703,W0622,W0613,W0201
import numpy as np

from pandas.core.dtypes.common import is_list_like
from pandas import compat
from pandas.core.arrays import Categorical

from pandas.core.dtypes.generic import ABCMultiIndex

from pandas.core.frame import _shared_docs
from pandas.util._decorators import Appender

import re
from pandas.core.dtypes.missing import notna
from pandas.core.tools.numeric import to_numeric


@Appender(_shared_docs['melt'] %
          dict(caller='pd.melt(df, ',
               versionadded="",
               other='DataFrame.melt'))
def melt(frame, id_vars=None, value_vars=None, var_name=None,
         value_name='value', col_level=None):
    # TODO: what about the existing index?
    if id_vars is not None:
        if not is_list_like(id_vars):
            id_vars = [id_vars]
        elif (isinstance(frame.columns, ABCMultiIndex) and
              not isinstance(id_vars, list)):
            raise ValueError('id_vars must be a list of tuples when columns'
                             ' are a MultiIndex')
        else:
            id_vars = list(id_vars)
    else:
        id_vars = []

    if value_vars is not None:
        if not is_list_like(value_vars):
            value_vars = [value_vars]
        elif (isinstance(frame.columns, ABCMultiIndex) and
              not isinstance(value_vars, list)):
            raise ValueError('value_vars must be a list of tuples when'
                             ' columns are a MultiIndex')
        else:
            value_vars = list(value_vars)
        frame = frame.loc[:, id_vars + value_vars]
    else:
        frame = frame.copy()

    if col_level is not None:  # allow list or other?
        # frame is a copy
        frame.columns = frame.columns.get_level_values(col_level)

    if var_name is None:
        if isinstance(frame.columns, ABCMultiIndex):
            if len(frame.columns.names) == len(set(frame.columns.names)):
                var_name = frame.columns.names
            else:
                var_name = ['variable_{i}'.format(i=i)
                            for i in range(len(frame.columns.names))]
        else:
            var_name = [frame.columns.name if frame.columns.name is not None
                        else 'variable']
    if isinstance(var_name, compat.string_types):
        var_name = [var_name]

    N, K = frame.shape
    K -= len(id_vars)

    mdata = {}
    for col in id_vars:
        mdata[col] = np.tile(frame.pop(col).values, K)

    mcolumns = id_vars + var_name + [value_name]

    mdata[value_name] = frame.values.ravel('F')
    for i, col in enumerate(var_name):
        # asanyarray will keep the columns as an Index
        mdata[col] = np.asanyarray(frame.columns
                                   ._get_level_values(i)).repeat(N)

    return frame._constructor(mdata, columns=mcolumns)


def lreshape(data, groups, dropna=True, label=None):
    """
    Reshape long-format data to wide. Generalized inverse of DataFrame.pivot

    Parameters
    ----------
    data : DataFrame
    groups : dict
        {new_name : list_of_columns}
    dropna : boolean, default True

    Examples
    --------
    >>> import pandas as pd
    >>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526],
    ...                      'team': ['Red Sox', 'Yankees'],
    ...                      'year1': [2007, 2007], 'year2': [2008, 2008]})
    >>> data
       hr1  hr2     team  year1  year2
    0  514  545  Red Sox   2007   2008
    1  573  526  Yankees   2007   2008

    >>> pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']})
          team  year   hr
    0  Red Sox  2007  514
    1  Yankees  2007  573
    2  Red Sox  2008  545
    3  Yankees  2008  526

    Returns
    -------
    reshaped : DataFrame
    """
    if isinstance(groups, dict):
        keys = list(groups.keys())
        values = list(groups.values())
    else:
        keys, values = zip(*groups)

    all_cols = list(set.union(*[set(x) for x in values]))
    id_cols = list(data.columns.difference(all_cols))

    K = len(values[0])

    for seq in values:
        if len(seq) != K:
            raise ValueError('All column lists must be same length')

    mdata = {}
    pivot_cols = []

    for target, names in zip(keys, values):
        to_concat = [data[col].values for col in names]

        import pandas.core.dtypes.concat as _concat
        mdata[target] = _concat._concat_compat(to_concat)
        pivot_cols.append(target)

    for col in id_cols:
        mdata[col] = np.tile(data[col].values, K)

    if dropna:
        mask = np.ones(len(mdata[pivot_cols[0]]), dtype=bool)
        for c in pivot_cols:
            mask &= notna(mdata[c])
        if not mask.all():
            mdata = {k: v[mask] for k, v in compat.iteritems(mdata)}

    return data._constructor(mdata, columns=id_cols + pivot_cols)


def wide_to_long(df, stubnames, i, j, sep="", suffix=r'\d+'):
    r"""
    Wide panel to long format. Less flexible but more user-friendly than melt.

    With stubnames ['A', 'B'], this function expects to find one or more
    group of columns with format Asuffix1, Asuffix2,..., Bsuffix1, Bsuffix2,...
    You specify what you want to call this suffix in the resulting long format
    with `j` (for example `j='year'`)

    Each row of these wide variables are assumed to be uniquely identified by
    `i` (can be a single column name or a list of column names)

    All remaining variables in the data frame are left intact.

    Parameters
    ----------
    df : DataFrame
        The wide-format DataFrame
    stubnames : str or list-like
        The stub name(s). The wide format variables are assumed to
        start with the stub names.
    i : str or list-like
        Column(s) to use as id variable(s)
    j : str
        The name of the subobservation variable. What you wish to name your
        suffix in the long format.
    sep : str, default ""
        A character indicating the separation of the variable names
        in the wide format, to be stripped from the names in the long format.
        For example, if your column names are A-suffix1, A-suffix2, you
        can strip the hyphen by specifying `sep='-'`

        .. versionadded:: 0.20.0

    suffix : str, default '\\d+'
        A regular expression capturing the wanted suffixes. '\\d+' captures
        numeric suffixes. Suffixes with no numbers could be specified with the
        negated character class '\\D+'. You can also further disambiguate
        suffixes, for example, if your wide variables are of the form
        Aone, Btwo,.., and you have an unrelated column Arating, you can
        ignore the last one by specifying `suffix='(!?one|two)'`

        .. versionadded:: 0.20.0

        .. versionchanged:: 0.23.0
            When all suffixes are numeric, they are cast to int64/float64.

    Returns
    -------
    DataFrame
        A DataFrame that contains each stub name as a variable, with new index
        (i, j)

    Examples
    --------
    >>> import pandas as pd
    >>> import numpy as np
    >>> np.random.seed(123)
    >>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
    ...                    "A1980" : {0 : "d", 1 : "e", 2 : "f"},
    ...                    "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},
    ...                    "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},
    ...                    "X"     : dict(zip(range(3), np.random.randn(3)))
    ...                   })
    >>> df["id"] = df.index
    >>> df
      A1970 A1980  B1970  B1980         X  id
    0     a     d    2.5    3.2 -1.085631   0
    1     b     e    1.2    1.3  0.997345   1
    2     c     f    0.7    0.1  0.282978   2
    >>> pd.wide_to_long(df, ["A", "B"], i="id", j="year")
    ... # doctest: +NORMALIZE_WHITESPACE
                    X  A    B
    id year
    0  1970 -1.085631  a  2.5
    1  1970  0.997345  b  1.2
    2  1970  0.282978  c  0.7
    0  1980 -1.085631  d  3.2
    1  1980  0.997345  e  1.3
    2  1980  0.282978  f  0.1

    With multuple id columns

    >>> df = pd.DataFrame({
    ...     'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
    ...     'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
    ...     'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
    ...     'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
    ... })
    >>> df
       birth  famid  ht1  ht2
    0      1      1  2.8  3.4
    1      2      1  2.9  3.8
    2      3      1  2.2  2.9
    3      1      2  2.0  3.2
    4      2      2  1.8  2.8
    5      3      2  1.9  2.4
    6      1      3  2.2  3.3
    7      2      3  2.3  3.4
    8      3      3  2.1  2.9
    >>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age')
    >>> l
    ... # doctest: +NORMALIZE_WHITESPACE
                      ht
    famid birth age
    1     1     1    2.8
                2    3.4
          2     1    2.9
                2    3.8
          3     1    2.2
                2    2.9
    2     1     1    2.0
                2    3.2
          2     1    1.8
                2    2.8
          3     1    1.9
                2    2.4
    3     1     1    2.2
                2    3.3
          2     1    2.3
                2    3.4
          3     1    2.1
                2    2.9

    Going from long back to wide just takes some creative use of `unstack`

    >>> w = l.unstack()
    >>> w.columns = w.columns.map('{0[0]}{0[1]}'.format)
    >>> w.reset_index()
       famid  birth  ht1  ht2
    0      1      1  2.8  3.4
    1      1      2  2.9  3.8
    2      1      3  2.2  2.9
    3      2      1  2.0  3.2
    4      2      2  1.8  2.8
    5      2      3  1.9  2.4
    6      3      1  2.2  3.3
    7      3      2  2.3  3.4
    8      3      3  2.1  2.9

    Less wieldy column names are also handled

    >>> np.random.seed(0)
    >>> df = pd.DataFrame({'A(quarterly)-2010': np.random.rand(3),
    ...                    'A(quarterly)-2011': np.random.rand(3),
    ...                    'B(quarterly)-2010': np.random.rand(3),
    ...                    'B(quarterly)-2011': np.random.rand(3),
    ...                    'X' : np.random.randint(3, size=3)})
    >>> df['id'] = df.index
    >>> df # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
       A(quarterly)-2010  A(quarterly)-2011  B(quarterly)-2010  ...
    0           0.548814           0.544883           0.437587  ...
    1           0.715189           0.423655           0.891773  ...
    2           0.602763           0.645894           0.963663  ...
       X  id
    0  0   0
    1  1   1
    2  1   2

    >>> pd.wide_to_long(df, ['A(quarterly)', 'B(quarterly)'], i='id',
    ...                 j='year', sep='-')
    ... # doctest: +NORMALIZE_WHITESPACE
             X  A(quarterly)  B(quarterly)
    id year
    0  2010  0      0.548814     0.437587
    1  2010  1      0.715189     0.891773
    2  2010  1      0.602763     0.963663
    0  2011  0      0.544883     0.383442
    1  2011  1      0.423655     0.791725
    2  2011  1      0.645894     0.528895

    If we have many columns, we could also use a regex to find our
    stubnames and pass that list on to wide_to_long

    >>> stubnames = sorted(
    ...     set([match[0] for match in df.columns.str.findall(
    ...         r'[A-B]\(.*\)').values if match != [] ])
    ... )
    >>> list(stubnames)
    ['A(quarterly)', 'B(quarterly)']

    All of the above examples have integers as suffixes. It is possible to
    have non-integers as suffixes.

    >>> df = pd.DataFrame({
    ...     'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
    ...     'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
    ...     'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
    ...     'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
    ... })
    >>> df
       birth  famid  ht_one  ht_two
    0      1      1     2.8     3.4
    1      2      1     2.9     3.8
    2      3      1     2.2     2.9
    3      1      2     2.0     3.2
    4      2      2     1.8     2.8
    5      3      2     1.9     2.4
    6      1      3     2.2     3.3
    7      2      3     2.3     3.4
    8      3      3     2.1     2.9

    >>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age',
                            sep='_', suffix='\w')
    >>> l
    ... # doctest: +NORMALIZE_WHITESPACE
                      ht
    famid birth age
    1     1     one  2.8
                two  3.4
          2     one  2.9
                two  3.8
          3     one  2.2
                two  2.9
    2     1     one  2.0
                two  3.2
          2     one  1.8
                two  2.8
          3     one  1.9
                two  2.4
    3     1     one  2.2
                two  3.3
          2     one  2.3
                two  3.4
          3     one  2.1
                two  2.9

    Notes
    -----
    All extra variables are left untouched. This simply uses
    `pandas.melt` under the hood, but is hard-coded to "do the right thing"
    in a typical case.
    """
    def get_var_names(df, stub, sep, suffix):
        regex = r'^{stub}{sep}{suffix}$'.format(
            stub=re.escape(stub), sep=re.escape(sep), suffix=suffix)
        pattern = re.compile(regex)
        return [col for col in df.columns if pattern.match(col)]

    def melt_stub(df, stub, i, j, value_vars, sep):
        newdf = melt(df, id_vars=i, value_vars=value_vars,
                     value_name=stub.rstrip(sep), var_name=j)
        newdf[j] = Categorical(newdf[j])
        newdf[j] = newdf[j].str.replace(re.escape(stub + sep), "")

        # GH17627 Cast numerics suffixes to int/float
        newdf[j] = to_numeric(newdf[j], errors='ignore')

        return newdf.set_index(i + [j])

    if any(col in stubnames for col in df.columns):
        raise ValueError("stubname can't be identical to a column name")

    if not is_list_like(stubnames):
        stubnames = [stubnames]
    else:
        stubnames = list(stubnames)

    if not is_list_like(i):
        i = [i]
    else:
        i = list(i)

    if df[i].duplicated().any():
        raise ValueError("the id variables need to uniquely identify each row")

    value_vars = [get_var_names(df, stub, sep, suffix) for stub in stubnames]

    value_vars_flattened = [e for sublist in value_vars for e in sublist]
    id_vars = list(set(df.columns.tolist()).difference(value_vars_flattened))

    melted = []
    for s, v in zip(stubnames, value_vars):
        melted.append(melt_stub(df, s, i, j, v, sep))
    melted = melted[0].join(melted[1:], how='outer')

    if len(i) == 1:
        new = df[id_vars].set_index(i).join(melted)
        return new

    new = df[id_vars].merge(melted.reset_index(), on=i).set_index(i + [j])

    return new
